WU-SN-TMC Bio-Analysis Core

WU-SN-TMC 生物分析核心

• 批准号: 10685428
• 负责人: FENG CHEN
• 金额: $ 82.65万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685428
• 关键词: 3-Dimensional; ATAC-seq; Age; Architecture; Atlases; Basic Science; Behavior; Biological; Biological Markers; Bone Marrow; Breast; Cell Aging; Cells; Characteristics; Clinical; Colon; Complex; Data; Data Analyses; Dimensions; Disease; Emerging Technologies; Environment; Epigenetic Process; Fluorescence Microscopy; Gene Expression Profile; Generations; Genes; Genetically Engineered Mouse; Goals; Human; Image; Individual; Infrastructure; Light; Liver; Longevity; Machine Learning; Maps; Methods; Microscopy; Molecular; Normal tissue morphology; Operative Surgical Procedures; Organ; Phenotype; Physiological; Positioning Attribute; Proteomics; Protocols documentation; Research; Resolution; Sampling; Site; Small Nuclear RNA; Spatial Distribution; Specificity; Specimen; Standardization; Stimulus; Technology; Testing; Time; Tissue atlas; Tissues; Translational Research; Universities; Washington; Work; age related; biomarker identification; biomarker signature; biomarker validation; body system; cell type; detection method; exome sequencing; experience; human disease; human tissue; improved; model organism; mouse model; multiple omics; new technology; novel strategies; novel therapeutics; scale up; senescence; sex; synergism; technology platform; therapeutic target; tissue mapping; transcriptome; transcriptome sequencing; transcriptomics; tumor; 3-Dimensional; ATAC-seq; Age; Architecture; Atlases; Basic Science; Behavior; Biological; Biological Markers; Bone Marrow; Breast; Cell Aging; Cells; Characteristics; Clinical; Colon; Complex; Data; Data Analyses; Dimensions; Disease; Emerging Technologies; Environment; Epigenetic Process; Fluorescence Microscopy; Gene Expression Profile; Generations; Genes; Genetically Engineered Mouse; Goals; Human; Image; Individual; Infrastructure; Light; Liver; Longevity; Machine Learning; Maps; Methods; Microscopy; Molecular; Normal tissue morphology; Operative Surgical Procedures; Organ; Phenotype; Physiological; Positioning Attribute; Proteomics; Protocols documentation; Research; Resolution; Sampling; Site; Small Nuclear RNA; Spatial Distribution; Specificity; Specimen; Standardization; Stimulus; Technology; Testing; Time; Tissue atlas; Tissues; Translational Research; Universities; Washington; Work; age related; biomarker identification; biomarker signature; biomarker validation; body system; cell type; detection method; exome sequencing; experience; human disease; human tissue; improved; model organism; mouse model; multiple omics; new technology; novel strategies; novel therapeutics; scale up; senescence; sex; synergism; technology platform; therapeutic target; tissue mapping; transcriptome; transcriptome sequencing; transcriptomics; tumor

Biological Analysis Core Summary/Abstract Senescence is implicated in many human diseases and the elimination of senescent cells in model organisms slows, and in some cases reverses many diseases. These findings raise hope that a detailed mechanistic understanding of senescence will lead to novel therapies to treat human disease. Despite the importance of cellular senescence, we lack robust methods to identify, isolate, and manipulate senescent cells. New approaches and technologies position our team to comprehensively identify senescent cells. This, in turn, will enable in-depth analyses of biomarkers, spatial distribution, behavior, and function of senescent cells across human tissues and time in a high throughput manner. The primary goal for the Biological Analysis Core (BAC) within Washington University Senescence Mapping Center (WU-SN-TMC) is to generate multi-omics and imaging data to build high resolution, multi-dimensional atlases of cellular senescence across tissue types and lifespan to facilitate basic and translational research. Although senescent cells are tracked by several known markers, it is increasingly clear that not all senescent cells express the same set of markers and the usefulness of individual senescent markers may be limited. Static universal tissue agnostic senescence markers may not exist. Instead, there are likely dynamic senescence signatures (gene expression patterns anchored by frequent, but unobligated presence of some of the “known” markers and others yet to be identified), that may differ with cell type, age, and environmental stimuli. We propose using cutting edge enabling technologies at necessary scale, depth, and resolution to identify such signatures while mapping senescent cells in various tissue environments at different ages. We will map senescent cells and their associated phenotypes in four tissues (bone marrow, breast, liver, and colon), chosen for their clinical importance, diversity, relevance to senescence, and availability. We will utilize the highly successful infrastructure we have developed for several large-scale projects as a springboard, combined with in-house expertise in senescence research, to generate cellular senescence atlases with transcriptome-wide coverage, single cell resolution, and spatial registration. We will use our experience in single cell omics to establish a comprehensive epigenetic and transcriptomic landscape in targeted tissues. We will add the spatial dimension to build 2D and 3D molecular atlases by incorporating spatial transcriptomics, CODEX, and light sheet microscopy. We will also use proteomics to study the biomarkers and secretome of senescent cells. To validate our results, we will use established and newly developed mouse models to track senescent cells at different ages with or without perturbations. These studies will allow us to improve detection methods for senescent cells and validate biomarker signatures identified in human studies. BAC will work closely with the other Cores within WU-SN-TMC to produce multi-dimensional molecular and cellular senescence tissue atlases at unprecedented cellular resolution and gene coverage with the highest efficiency possible.

生物分析核心摘要/摘要 衰老是在许多人类疾病中实施的，并消除了模型生物中的感觉细胞 放慢脚步，在某些情况下会扭转许多疾病。这些发现引起了人们的希望，即详细的机制 对感应的理解将导致治疗人类疾病的新疗法。尽管很重要 细胞感应，我们缺乏识别，分离和操纵感觉细胞的强大方法。新的 方法和技术将我们的团队定位为全面识别感官细胞。反过来将 对生物标志物，空间分布，行为和功能进行深入分析 人体组织和时间以高通量方式。生物分析核心（BAC）的主要目标 在华盛顿大学衰老映射中心（WU-SN-TMC）是为了产生多派和 成像数据以构建跨组织类型和 寿命促进基础和翻译研究。尽管有几个已知的感官细胞是跟踪的 标记，越来越明显的是，并非所有的感觉细胞都表达相同的标记和有用性 单个感应标记物可能受到限制。静态通用组织不可知的感应标记可能不会 存在。取而代 但是，某些“已知”标记的存在，而其他尚未确定），这可能会有所不同 细胞类型，年龄和环境刺激。我们建议在必要时使用尖端启用技术 在绘制各种组织中的感觉细胞的同时，识别此类特征的尺度，深度和分辨率 不同年龄的环境。我们将在四个组织中绘制感觉细胞及其相关的表型 （骨髓，乳房，肝脏和结肠），以其临床重要性，多样性和相关性而选择 感应和可用性。我们将利用我们为几个开发的非常成功的基础架构 大型项目作为跳板，结合了感应研究的内部专业知识，以产生 细胞感应地图酶具有整个转录组覆盖率，单细胞分辨率和空间登记。 我们将利用单元格中的经验来建立全面的表观遗传学和转录组学 目标组织中的景观。我们将添加空间维度，以构建2D和3D分子图谱 合并空间转录组学，法典和光片显微镜。我们还将使用蛋白质组学研究 感觉细胞的生物标志物和分泌组。为了验证我们的结果，我们将使用已建立和新的 开发了小鼠模型，以在不同年龄的情况下跟踪有或没有扰动的感觉细胞。这些研究 将使我们能够改善感官细胞的检测方法，并验证在 人类研究。 BAC将与WU-SN-TMC中的其他核心紧密合作，以产生多维 在前所未有的细胞分辨率和基因覆盖范围内的分子和细胞敏感组织图谱 最高效率。